《A Comprehensive Survey of LLM Alignment Techniques: RLHF, RLAIF, PPO, DPO and More》翻译与解读 Abstract With advancements in self-supervised learning, the availability of trillions tokens in a pre-training corpus, instruction fine-tuning, and the development of large Transformers with billions of...
论文标题:Evaluating Large Language Models: A Comprehensive Survey 论文地址:arxiv.org/abs/2310.1973评估中关键进展和局限性。此外,之前的综述主要集中在LLM的一致性评估上。 这篇综述扩大了范围,综合了LLMs的能力和一致性评估的研究结果。通过综合的观点和扩展的范围,综述工作在补充了这些先前的综述,提供了LLM评估...
利用反馈来自LLM来引导检索器的训练目标也可以有效地增强检索器适用于LLM的能力。鉴于LLMs的强大能力和表达潜力,基于LLM的密集检索最近已成为一个关键的研究领域和探索方向。LLM2vec修改了预先训练的LLM中的注意力机制为双向的,并采用了掩码下个词预测的方法来进行无监督训练,从而产生了一个基于LLM的密集检索嵌入器。...
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and BeyondarXiv26 Apr 2023Paper Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive SurveyarXiv30 May 2023Paper Several categories of Large Language Models (LLMs): A Short SurveyarXiv05 Jul ...
A Survey on Data Selection for LLM Instruction Tuning, arXiv 2024.02 [Paper] A Survey on Knowledge Distillation of Large Language Models, arXiv 2024.02 [Paper] Evaluation Evaluating Large Language Models: A Comprehensive Survey, arXiv 2023.10 [Paper] [GitHub] ...
However, it’s important to understand here that there are two different parts to those models — thebody or baseof the model and itshead.[2] Thebody or baseof an LLM model is a number of hidden layers that appear in the transformer’s architecture that are specialized to understand the...
The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual reasoning, as causality reveals the underlying data distribution. However, the lack of a comprehensive benchmark currently limits the evaluation of LLMs' causal...
TRANSLATING THE U.S. LLM EXPERIENCE: THE NEED FOR A COMPREHENSIVE EXAMINATIONBerkeley Electronic Press Selected WorksCarole SilverMayer FreedSilver, C, and Freed, M. (2006). Translating the US LLM experience: The need for a comprehensive examination. Northwestern University Law Review Colloquy 101,...
Small errors could lead to harm, revealing the LLMs' lack of actual comprehension despite advances in self-learning. This paper presents a comprehensive survey of over thirty-two techniques developed to mitigate hallucination in LLMs. Notable among these are Retrieval-Augmented Generation (RAG) (...
parameters of the reference model with exponential moving average. 结论是: Advantage normalization stabilizes PPO training and improves the performance of PPO. The most significant benefit is brought by using a large batch size, especially on code generation tasks using the exponential moving average fo...